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Related Experiment Video

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Camouflaged Instance Segmentation In-the-Wild: Dataset, Method, and Benchmark Suite.

Trung-Nghia Le, Yubo Cao, Tan-Cong Nguyen

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|December 2, 2021

Summary

This summary is machine-generated.

This study introduces CAMO++, a new dataset for camouflaged instance segmentation, and a camouflage fusion learning (CFL) framework. These resources advance the task of identifying camouflaged objects in diverse, real-world images.

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Area of Science:

Computer Vision
Artificial Intelligence
Image Analysis

Background:

Camouflaged object segmentation is challenging due to natural concealment.
Existing datasets lack diversity and detailed annotations for in-the-wild images.
A robust benchmark is needed for evaluating camouflaged instance segmentation methods.

Purpose of the Study:

To introduce a new task: camouflaged instance segmentation for in-the-wild images.
To present CAMO++, an expanded dataset with hierarchical pixel-wise ground truths.
To provide a benchmark suite and evaluate existing methods.

Main Methods:

Developed the CAMO++ dataset, extending the original CAMO dataset.
Created a benchmark suite for camouflaged instance segmentation.
Proposed a camouflage fusion learning (CFL) framework.

Main Results:

CAMO++ offers increased quantity and diversity of images with detailed ground truths.
Extensive evaluation of state-of-the-art instance segmentation methods was performed.
The CFL framework demonstrated improved performance on camouflaged instance segmentation.

Conclusions:

The CAMO++ dataset and benchmark suite facilitate research in camouflaged instance segmentation.
The CFL framework offers a promising approach for enhancing segmentation accuracy.
Public release of dataset, model, and benchmark will foster further advancements.